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Codes Combine Co (047770) Fair Value & Analysis

Consumer Cyclical · KR · Market cap 106B KRW

Price2,870 KRW
Fair Value1,325 KRW
Upside-53.8%
Quality92/100
Evidence: High Range 987.80 KRW – 1,662 KRW

Fair value as of: Jun 24, 2026

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Analysis

Codes Combine Co (047770) currently trades at 2,870 KRW, while our model-based Fair Value estimate is 1,325 KRW — implying the stock looks roughly 53.8% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Cyclical sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: high).

About the company

Codes Combine Co., Ltd. manufactures and sells fashion clothing products. It also operates stores. The company was formerly known as Yeshin PJ Co., Ltd. Codes Combine Co., Ltd. was founded in 1995 and is headquartered in Seoul, South Korea.

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Frequently asked questions

Is Codes Combine Co (047770) undervalued?
As of Jun 24, 2026, our model estimates a fair value of 1,325 KRW versus a price of 2,870 KRW — about −54% (overvalued). Model-based estimate, not financial advice.
What is the fair value of 047770?
Our 21-model fair value for Codes Combine Co is 1,325 KRW (as of Jun 24, 2026), built from audited fundamentals. The current price is 2,870 KRW.
What is the quality score of 047770?
Codes Combine Co has a Quality Score of 92/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

How we calculate Fair Value

Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.

Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.